In the condition monitoring and fault diagnosis, useful information about the incipient fault features in the measured signal is always corrupted by noise. Fortunately, the Kalman filtering technique can filter the noise effectively, and the impending system fault can be revealed to prevent the system from malfunction. This paper has discussed recent progress of the Kalman filters for the condition monitoring and fault diagnosis. A case study on the rolling bearing condition monitoring and fault diagnosis using Kalman filter and support vector machine (SVM) has been presented. The analysis result showed that the integration of the Kalman filter and SVM was feasible and reliable for the rolling bearing condition monitoring and fault diagnosis and the fault detection rate was over 96.5%.

The motion commands to feed drives constitute a major source for excitation of the vibrations. In this paper the vibration of feed drive caused by the motion commands is simulated by using a mathematic method. The mechanical transmission system is modeled as a dual inertia system. Three typical trajectories are used to analyze the effect of motion profiles on feed drive vibration by using a simulation model of feed drive controller.